The article critiques the growing trend of using “Skills” as a standard for enabling capabilities in large language models (LLMs). The author prefers the Model Context Protocol (MCP), highlighting its advantages over Skills. MCP acts as an API abstraction, allowing LLMs to interact with services without needing to know the underlying implementation. This approach eliminates the need for local installations, ensures seamless updates, and simplifies authentication, making it more user-friendly. The author expresses frustration with Skills, particularly when they require command-line interfaces (CLIs). Many LLMs, like ChatGPT, lack the ability to run CLIs, which limits the usefulness of Skills that depend on them. The article outlines various issues with Skills, such as deployment challenges, secret management complications, and context bloat, which can hinder user experience. In contrast, MCP provides a cleaner and more efficient way to connect LLMs to services, promoting a smoother interaction model. The author argues for a clear distinction in when to use MCP versus Skills. MCP should be the go-to for any service integration, allowing applications to dictate their interfaces. Skills, on the other hand, should focus on knowledge transfer rather than execution. The article suggests that terminology might be part of the problem, proposing a shift in naming to better reflect the functions of each approach. The emphasis remains on building efficient connectors rather than relying on cumbersome manuals for every interaction.